Comments (5)
Hey @vertesy , how did you get rPCA to run with SCT normalization? I've been struggling with this for weeks now, would infinitely appreciate any input
from seurat.cbe.issues.
Relevant issues:
I think the issue here is the use of large numbers of genes for features.to.integrate. This creates a non-sparse matrix for all genes, and is infeasible for any method - its not a specific problem with the Seurat alignment workflow. We do not suggest batch-correcting all genes, only ones that exhibit variation across single-cells, which are informative for downstream clustering analyses.
Thanks for the question - we've explored this and the cause is that there are so many anchors, that it creates a sparse matrix with >2^31 elements in R, which can throw an error.
This happens to me when I give a large number of genes for features.to.integrate.
I don't think this is Seurat's problem, but the problem with Matrix, which still doesn't support vectors with more than 2^31 elements. It's just that a sparse matrix with too many non-zero elements is produced. This can be worked around by using the sparse matrix package spam64, but will require changes to Seurat's source code. Actually supporting long vectors is on the to do list of Matrix developers, but somehow they still haven't implemented it.
from seurat.cbe.issues.
Suggestions
- Make 1 dataset reference
Addreference = 1
toanchors <- FindIntegrationAnchors(seus, normalization.method = "SCT", anchor.features = features_use, reference = 1)
. - Use RPCA -> see error in #8
- Decrease the number of genes for features.to.integrate. (debated, error at 1000) -> will try
- Not specifying anything for dims or
features.to.integrate
.
IntegrateData( anchorset = scData.Anchors )
from seurat.cbe.issues.
- makes an invalid assumption to our analysis → NO
- rPCA finally worked, but it was very tough to get it run
- "Decrease the number of genes" did not solve it → NO
- Defaults did not solve it → NO
from seurat.cbe.issues.
Hi, I am facing the same problem, my dataset is around 122k cells from 32 samples. Both methods fail (CCA and rPCA) when it comes to integratedata() step. I tried as low as 1k variable features but it's not working. How did you get rPCA method to work?
Note: both methods work fine when integrating using the previous normalization methods (log2 norm)
from seurat.cbe.issues.
Related Issues (10)
- ToDo
- Wiki
- Resources
- Error in Calcq90Expression: error 'problem too large' HOT 3
- I cannot see history in TMUX / R console because I cannot scroll up HOT 2
- Token based sourcing from private repo does not work on CBE
- Error: Failed to retrieve the result of MulticoreFuture (future_lapply-21) in FindIntegrationAnchors HOT 6
- Error in differential gene expression – replacement has 0 rows HOT 1
- installing monocle3 HOT 4
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from seurat.cbe.issues.